Application of biological simulation models in estimating feed efficiency of finishing steers

Analyses of plasma metabolites using a high performance four-channel CIL LC-MS method and identification of metabolites associated with enteric methane emissions in beef cattle

Reducing enteric methane (one greenhouse gas) emissions from beef cattle not only can be beneficial in reducing global warming, but also improve efficiency of nutrient utilization in the production system. However, direct measurement of enteric methane emissions on individual cattle is difficult and expensive. The objective of this study was to detect plasma metabolites that are associated with enteric methane emissions in beef cattle. Average enteric methane emissions (CH4) per day (AVG_DAILYCH4) for each individual cattle were measured using the GreenFeed emission monitoring (GEM) unit system, and beef cattle with divergent AVG_DAILYCH4 from Angus (n = 10 for the low CH4 group and 9 for the high CH4 group), Charolais (n = 10 for low and 10 for = high), and Kinsella Composite (n = 10 for low and 10 for high) populations were used for plasma metabolite quantification and metabolite-CH4 association analyses. Blood samples of these cattle were collected near the end of the GEM system tests and a high performance four-channel chemical isotope labeling (CIL) liquid chromatography (LC) mass spectrometer (MS) method was applied to identify and quantify concentrations of metabolites. The four-channel CIL LC-MS method detected 4235 metabolites, of which 1105 were found to be significantly associated with AVG_DAILYCH4 by a t-test, while 1305 were significantly associated with AVG_DAILYCH4 by a regression analysis at p<0.05. Both the results of the t-test and regression analysis revealed that metabolites that were associated with enteric methane emissions in beef cattle were largely breed-specific whereas 4.29% to 6.39% CH4 associated metabolites were common across the three breed populations and 11.07% to 19.08% were common between two breed populations. Pathway analyses of the CH4 associated metabolites identified top enriched molecular processes for each breed population, including arginine and proline metabolism, arginine biosynthesis, butanoate metabolism, and glutathione metabolism for Angus; beta-alanine metabolism, pyruvate metabolism, glycolysis / gluconeogenesis, and citrate cycle (TCA cycle) for Charolais; phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arginine biosynthesis, and arginine and proline metabolism for Kinsella Composite. The detected CH4 associated metabolites and enriched molecular processes will help understand biological mechanisms of enteric methane emissions in beef cattle. The detected CH4 associated plasma metabolites will also provide valuable resources to further characterize the metabolites and verify their utility as biomarkers for selection of cattle with reduced methane emissions.

Characterization of feed efficiency-related key signatures molecular in different cattle breeds

Feed efficiency is a major constraint in the beef industry and has a significant negative correlation with residual feed intake (RFI). RFI is widely used as a measure of feed efficiency in beef cattle and is independent of economic traits such as body weight and average daily gain. However, key traits with commonality or specificity among beef cattle breeds at the same level of RFI have not been reported. Accordingly, the present study hypothesized that signatures associated with feed efficiency would have commonality or specificity in the liver of cattle breeds at the same RFI level. By comparing and integrating liver transcriptome data, we investigated the critical signatures closely associated with RFI in beef cattle using weighted co-expression network analysis, consensus module analysis, functional enrichment analysis and protein network interaction analysis. The results showed that the consensus modules in Angus and Charolais cattle were negatively correlated, and four (turquoise, red, tan, yellow) were significantly positively correlated in Angus liver, while (turquoise, red) were significantly negatively correlated in Charolais liver. These consensus modules were found to be primarily involved in biological processes such as substance metabolism, energy metabolism and gene transcription, which may be one of the possible explanations for the difference in feed efficiency between the two beef breeds. This research also identified five key candidate genes, PLA2G12B, LCAT, MTTP, LCAT, ABCA1 and FADS1, which are closely associated with hepatic lipid metabolism. The present study has identified some modules, genes and pathways that may be the major contributors to the variation in feed efficiency among different cattle breeds, providing a new perspective on the molecular mechanisms of feed efficiency in beef cattle and a research basis for investigating molecular markers associated with feed efficiency in beef cattle.

Integrating Genomics with Nutrition Models to Improve the Prediction of Cattle Performance and Carcass Composition under Feedlot Conditions

Cattle body composition is difficult to model because several factors affect the composition of the average daily gain (ADG) of growing animals. The objective of this study was to identify commercial single nucleotide polymorphism (SNP) panels that could improve the predictability of days on feed (DOF) to reach a target United States Department of Agriculture (USDA) grade given animal, diet, and environmental information under feedyard conditions. The data for this study was comprised of crossbred heifers (n = 681) and steers (n = 836) from commercial feedyards. Eleven molecular breeding value (MBV) scores derived from SNP panels of candidate gene polymorphisms and two-leptin gene SNP (UASMS2 and E2FB) were evaluated. The empty body fat (EBF) and the shrunk body weight (SBW) at 28% EBF (AFSBW) were computed by the Cattle Value Discovery System (CVDS) model using hip height (EBF_HH and AFSBW_HH) or carcass traits (EBF_CT and AFSBW_CT) of the animals. The DOF_HH was calculated when AFSBW_HH and ADG_HH were used and DOF_CT was calculated when AFSBW_CT and ADG_CT were used. The CVDS estimates dry matter required (DMR) by individuals fed in groups when observed ADG and AFSBW are provided. The AFSBW_CT was assumed more accurate than the AFSBW_HH because it was computed using carcass traits. The difference between AFSBW_CT and AFSBW_HH, DOF_CT and DOF_HH, and DMR and dry matter intake (DMI) were regressed on the MBV scores and leptin gene SNP to explain the variation. Our results indicate quite a large range of correlations among MBV scores and model input and output variables, but MBV ribeye area was the most strongly correlated with the differences in DOF, AFSBW, and DMI by explaining 8, 13.2 and 6.5%, respectively, of the variation. This suggests that specific MBV scores might explain additional variation of input and output variables used by nutritional models in predicting individual animal performance.

Estimating feed efficiency: evaluation of mathematical models to predict individual intakes of steers fed in group pens

To evaluate feed efficiency using residual feed intake (RFI), it is necessary to measure and record daily feed intake for each animal. This can be accomplished by housing them in individual pens or by using sophisticated electronic feeders in group pens. All the available options are very expensive and very laborious; therefore, several researchers have developed methods to predict individual DMI of cattle fed in group pens. Three intake models were tested with a data set of 60 Angus × Hereford steers fed a corn-based finishing diet in both group and individual pens. After the first 60 d (period 1) of the study, animals were switched from group to individual pens, and then vice versa for another 60 d (period 2); thus, the entire feeding trial was 120 d long. No difference was observed in DMI between periods for steers fed individually (period 1 = 10.9 kg/d and period 2 = 11.2 kg/d, P = 0.44), but a difference was observed in group pens (period 1 = 12.7 kg/d and period 2 = 10.9 kg/d, P < 0.01). In addition, no difference (P ≥ 0.15) was observed in carcass characteristics, such as HCW, dressing percentage, quality grade, LM area, KPH percentage, yield grade, or backfat between RFI groups (low, medium, and high). Average daily gain and G:F were not different between RFI groups within each period (P ≥ 0.06), but there were period differences (P < 0.001). Models 1 and 2 were based on growth, carcass composition, and nutrient requirements, whereas model 3 was based on the heterogeneity of pen intakes when cattle were rotated through the pens on a daily basis. Models 1 and 2 were forced through the mean observed DMI, so the mean bias was zero, but they were not precise, with a slope bias greater than 50%. Model 3 showed low accuracy (mean bias = 20%), but it was precise, with a slope bias of 21%. Because RFI is the error of the DMI equation, any inaccuracy when estimating intake will lead to a bias in the prediction of RFI. In conclusion, these models could be used to predict mean DMI, but they were not adequate for estimating RFI.